Random Activations in Primal-Dual Splittings for Monotone Inclusions with a Priori Information

Autor: Cristian Vega, Luis M. Briceño-Arias, Julio Deride
Rok vydání: 2021
Předmět:
Zdroj: Journal of Optimization Theory and Applications volume 192, issue 1, pages 56–81 (2022)
ISSN: 1573-2878
0022-3239
DOI: 10.1007/s10957-021-01944-6
Popis: In this paper, we propose a numerical approach for solving composite primal-dual monotone inclusions with a priori information. The underlying a priori information set is represented by the intersection of fixed point sets of a finite number of operators, and we propose and algorithm that activates the corresponding set by following a finite-valued random variable at each iteration. Our formulation is flexible and includes, for instance, deterministic and Bernoulli activations over cyclic schemes, and Kaczmarz-type random activations. The almost sure convergence of the algorithm is obtained by means of properties of stochastic Quasi-Fej\'er sequences. We also recover several primal-dual algorithms for monotone inclusions in the context without a priori information and classical algorithms for solving convex feasibility problems and linear systems. In the context of convex optimization with inequality constraints, any selection of the constraints defines the a priori information set, in which case the operators involved are simply projections onto half spaces. By incorporating random projections onto a selection of the constraints to classical primal-dual schemes, we obtain faster algorithms as we illustrate by means of a numerical application to a stochastic arc capacity expansion problem in a transport network.
Comment: 23 Pages, 4 figures, 4 Tables
Databáze: OpenAIRE
Externí odkaz: